Sales Intelligence Blog by Lattice Engines

In “Scorecasting: The Hidden Influences Behind How Sports Are Played and Games Are Won”, Tobias Moskowitz (an economist) and L. Jon Wertheim (Sports Illustrated writer) take an analytical approach to assess and quantify sports phenomena like “home field advantage” and “over-paying first round draft picks.”

One of the more compelling chapters uses mathematics and game theory to explain why football teams (professional, college, prep) are generally better off foregoing a punt or field goal attempt and should “go for it” more often on fourth down.  The authors argue that football coaches consistently misunderstand the likely outcome achieved with letting the offense play fourth down, and opt for a conservative course that keeps scores low and punters employed.  They cite coaches like Bill Belichick and others in the college ranks that (statistically) opt to go for it on fourth down more often than colleagues do – and have achieve greater levels of success.  The book suggests we take a second look at our assumptions and expectations to make better decisions that will “put more points on the board.”

We have noticed sales leaders increasingly ask their organizations for more data about their market opportunity, their performance against that opportunity and the effectiveness of various sales motions.  Sometimes this can mean changing the central premise on which a function operates.  For example, sales leaders focused on driving higher new customer acquisition rates from “leads” have looked at new ways to enable reps to proactively contact a prospect – relying less on “hand raising” and more on data suggesting a prospect has a near-term business need. 

What are the elements of your sales process that need new thinking?  Do you punt or kick field goals when you should have a different set of players on the field?

Share and Enjoy:

• 

Sales people are the quintessential sports lovers, and it is a rare meeting that does not make generous use of metaphors and anecdotes borrowed from the game of Baseball. Baseball season is not in full swing yet, there is something really exciting going on across the world – the Cricket World Cup!

With over two Billion people in Australia, Bangladesh, Canada (yes Canada!), Netherlands, Ireland, England, Pakistan, Sri Lanka, West Indies, India, Zimbabwe, South Africa, New Zealand and Kenya paying attention to the event, you can’t go wrong learning a little bit about the sport and the parallels with Baseball.

Let’s start with the similarities. Both Cricket and Baseball are played between two teams taking turns with a “bat” and a “ball”. Throwing the ball is called “bowling” in cricket while the ball is “pitched” in Baseball. Both the games involve something being defended (the “Strike Zone” for baseball is equivalent to the “wicket” for cricket) against the ball. Once the ball is hit with a bat, the batters (batsmen) run from one base (wicket) to another. If the ball is caught before it hits the ground, the batter (batsman) is deemed out and someone else takes their place. The goal is to score as many runs as possible in each case, and the team scoring more runs wins. So far so good!

Now for the Differences:

• The way runs and outs are defined: In cricket the team clocks a run every-time the players run from one wicket (base) to another. Baseball runs only count when the player completes all the bases. Cricket outs come in many varieties (run out, caught, bowled, leg before wicket, stumped, and hit wicket). As you would expect from a game invented by landed English gentry, there are other obscure ways to get out like handling the ball, hitting the ball twice (go figure!), obstructing the field and timing out.
• The structure of the game: A cricket team has eleven players, and each player gets to bat until at least ten of them get out, or the number of pitches (balls) run out. The World cup format is called the one-day game, compared to more genteel five-day tests – originally funded by colonial economics. In this format, each side gets to play one inning each which is limited to a maximum of 300 balls (pitches) grouped together in 50 overs (an over has six balls).
• The way the ball gets to the batsman (batter): In cricket, you are allowed to bounce the ball off the ground before it gets to the bat. This means the condition of the field, the soil, the moisture level, the amount of grass on it, makes a huge difference in the speed and turn with which the ball arrives at the batsman. Since soil conditions are hard to quantify, this also makes the game somewhat more unpredictable and harder to model.

Lastly, Baseball has come to rely on predictive analytics to shape their teams and their game.  Ever since Michael Lewis wrote the excellent book Moneyball about the Oakland A’s and their manager Billy Beane’s predictive models of player performance, there has also been an increasing interest in the use of data to build world-class teams. There were many insights that emerged from the A’s analyses, but the one that really stuck were the importance of “on-base percentage” and “slugging percentage”, both of which were found to be better predictors of player performance than “runs batted in” or “batting average”.

Cricket is not so easy to predict.  Take for example what is probably the greatest upset ever in Cricket history – the Ireland-England game played on March 2nd, 2011.

With a long history and a solid line-up, England were 20/1 favorites going into the game. They had just drawn the game with one the perennial favorites – India, and were looking to wrap up the game with Ireland quickly.

The English team batted first (remember there is only one inning), and scored 328 runs. Eight of their players got out before they ran out of their 300 balls (50 overs). The top scorer was Jonathan Trott, a 29-year old, with 92 runs made off 92 balls. This means he ran between the two wickets 92 times – this by the way is why the Cricket players are in better shape than their Baseball counterparts. His strike-rate (SR) was 100%, as in he scored a run for every ball he faced. Not bad!!

328 runs is actually a lot of runs, even for Cricket. The usual heuristic is that if you bat first and score less than 200 (4 runs per over) you are in trouble, while a 300+ score (>6 runs per over) puts you in the safe zone. Anything in between leaves the match open. So England going in with 328 must have felt pretty secure about the outcome.

Ireland started poorly, losing their first 5 wickets (getting 5 outs) for 111 runs, with only 154 balls remaining. Since they had to score 329-111 = 218 more runs to win, they now needed to score at a strike-rate of 141%, which is an impossible target for almost any team. Well not for Kevin O’ Brien from Dublin, Ireland. He scored 113 runs off 63 balls at a strike-rate of 179%, that’s right – he scored almost 2 runs for every ball he faced. The English team didn’t know what hit them, as the Irish wrapped up the game with a score of 329, having lost only 7 wickets (3 outs still to go) and 5 balls remaining.

I have often heard the refrain from my friends that they don’t like soccer because it is too low-scoring. Well if you want a really high-scoring sport, Cricket could be the answer.

So what does all this have to do with B2B Sales Intelligence? Not much, except that the definitive “Moneyball” for Cricket or for Sales Teams is yet to be written. Our software helps the Sales side, but Cricket deserves its Billy Beane too. I hope someone will explain how to spot the next Sachin Tendulkar, Wasim Akram or Don Bradman. That will make the game a lot more interesting, maybe even to Baseball lovers.

In a future version I plan on writing the complimentary “Baseball for Cricket lovers” but we will have to wait till the World Series for that.

****************************************************

For those interested in more details, please see the detailed Wikipedia article on the subject. I have borrowed some of the images and content from this source.

Special thanks to Geoff Zassenhaus, a crazy Red-Sox fanatic (is there any other type?), who has been patiently explaining Baseball to me for over fifteen years. In memory of the late Partha Niyogi, my brother-in-law and friend, who could have written the “Moneyball” for Cricket. His love of the math(s) of Cricket was infectious; I miss our late-night conversations about how to use statistics and machine-learning to find outliers in cricket and elsewhere.

Share and Enjoy:

•